Knowledge Activity: MPI Duplication Analysis (Informatics)
Part 1: Generate the Dataset Using the Queries Tool
Access the EHR queries tool and corresponding dataset under 2: Launch EHR. Click the New Session button to launch the EHR.
On the Queries tab, select New to initiate a new query.
· First, Name the query ‘ MPI Duplication Analysis’
· Then enter the following query rule:
Query rule(s): ‘Duplicate Threshold’ equal ‘Y’
· Select Add rule to add another rule and select OR as the logic
· Then enter the following second rule:
Query rule(s): ‘Duplicate Threshold’ equal ‘N’
· Select Query to generate the results. There should be 526 total records. If not, review the screenshot above to ensure your query is setup correctly. If necessary, adjust and re-run the query.
· Select Export Results to Excel to export your dataset for further analysis.
Part 2: Analyze the Data in Microsoft Excel®
Open the exported file in Microsoft Excel®.
In order to analyze the possible duplicates, use the Sort function.
· Select Data then Sort. Enter ‘Duplicate Threshold’ in the Sort by field. Then select ‘Z to A’ for the Order field. Then click OK.
Those with “Y” (yes) as the Duplicate Threshold will now be listed first in the spreadsheet.
Of those that are possible duplicates (Y’s), group the possible duplicates together for analysis. To do so, build on the previous sort by incorporating the ‘Possible match code’ data.
· Go to Data then Sort. Your previous sort parameters will still be listed. Choose Add level and select ‘Possible match code’ as the column and ‘A to Z’ as the Order then OK.
The resulting spreadsheet will have the possible duplicate entries grouped together for further review. For example, the possible match code of “C” results are highlighted – a potential duplicate pair requiring further analysis.
Review all possible duplicate matches in the resulting spreadsheet and answer the following questions. Round to the nearest percent when asked for a percentage.
1. How many of the results are possible duplicates?
2. What percentage of the registrations are potential duplicates?
3. How many of the potential duplicate pairs have first name discrepancies? What is the percentage of first name discrepancies in the duplicate pairs?
4. What is the most common cause of first name discrepancies?
5. How many of the potential duplicate pairs have middle name discrepancies? What is the percentage of middle name discrepancies in the duplicate pairs?
6. What is the most common cause of middle name discrepancies?
7. How many of the potential duplicates have last name discrepancies? What is the percentage of last name discrepancies?
8. What is the most common cause of last name discrepancies?
9. How many of the potential duplicates have gender discrepancies?
10. What are the causes of the gender discrepancies?
11. How many of the potential duplicates have DOB discrepancies?
12. What is the most common cause of DOB discrepancies?
13. How many of the potential duplicates have mismatches of MRN?
14. What is the most common cause of mismatched MRN?
15. How many of the potential duplicates have mismatches of SSN?
16. What is the most common cause of mismatched SSN?
Blank and default fields are a common cause of duplicate record creation. Analyze the resulting possible duplicates for blank and default fields. Use the sort function again to determine which employees were responsible for entering the data containing blank or default entries.
· Go to Data and Sort
· Delete the existing level based on Possible match code (keep the Duplicate Threshold criteria)
· Add level and choose ‘Registered By’ as the column then ‘A to Z’ as the Order. Then click OK.
Of the employees listed below, how many blank or default (MRXXXXXX or XXX-XX-XXXX) entries were each responsible for creating? If there were multiple blank or default entries for one registration, only count that registration once.
17. Dvaughn Lane, LPN
18. Cora Zimmerman, MA
19. Kim Pham, LPN
20. Emily Garcia, MA
21. Clara Rojo, RHIT
22. Wanda Murray, RHIA
23. Tina Lin, HUC
24. Kevin Fanning, MA
25. Who created the most duplicate records by leaving default or blank values in the patient registration data?
Recommendations for decreasing duplicate record formation
After analyzing the Hospital’s duplicate records, you are asked to provide recommendations for improvement. Please review the MPI Duplicate Resolution resource included under 1: Overview & Resources along with this activity document to assist in answering the questions below.
26. What else could the General Hospital implement to prevent duplicate records from getting created?
27. What are potential negative impacts of duplicate records?
28. What can HIIM professionals do to prevent and manage duplicate records?
29. What is an overlaid record?
30. What is the best way to prevent overlaid records?
31. According to the Challenges in MPI Duplicate Resolution resource found under 1: Overview & Resources along with this activity document, which of the following is a data-related reason for duplicate records?
a. Lack of data standardization and transposition of the month and date in the DOB
b. Changing demographics and nicknames
c. Default and null values in key identifying fields and lack of data standardization
d. Frequently changing mobile phone numbers
32. What are three strategies suggested by the Challenges in MPI Duplicate Resolution resource to prevent duplicate records from being formed?
33. List four recommendations to provide to the General Hospital to help prevent duplicate records from being created.
Submit your work
Document your answers directly on this activity document as you complete the activity. When you are finished, save this activity document to your device and upload this activity document with your answers to your Learning Management System (LMS). If you have any questions about submitting your work to your LMS, please contact your instructor.
Learning objectives
1. Identify the types of duplicate records found in an MPI.
2. Analyze common causes for duplicate records.
3. Identify strategies to address and prevent the creation of duplicate records in an MPI.
4. Analyze strategies for the management of information. (4)
5. Recommend compliance of health record content across the health system. (5)
6. Evaluate data dictionaries and data sets for compliance with governance standards. (5)
7. Facilitate training needs for a healthcare organization. (4)
References
AHIMA. Fundamentals for Building a Master Patient Index/Enterprise Master Patient Index. Journal of AHIMA (Updated September 2010).
Haenke Just, B., Marc, D., Munns, M., & Sandefer, R. (2016). Why Patient Matching Is a Challenge: Research on Master Patient Index (MPI) Data Discrepancies in Key Identifying Fields. Washington D.C.: AHIMA.
Harris, S., & Houser, S. H. (2018). Double Trouble—Using Health Informatics to Tackle Duplicate Medical Record Issues. Journal of AHIMA 89, no. 8, 20-23.
EHR Go Knowledge Activity: MPI Duplication Analysis (Informatics) HIK1046.3
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